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Record W2591886005 · doi:10.1080/21650020.2017.1295817

Assessing the accessibility of activity centres and their prioritisation: a case study for Perth Metropolitan Area

2017· article· en· W2591886005 on OpenAlexaff
Md Moniruzzaman, Doina Olaru, Sharon Biermann

Bibliographic record

VenueUrban Planning and Transport Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Toronto
FundersCurtin University of TechnologyBritish Medical Association
KeywordsMetropolitan areaPublic transportGeographyDecentralizationTransport engineeringRegional scienceGovernment (linguistics)Environmental planningConsolidation (business)Geographic information systemBusinessCartographyEngineeringPolitical science

Abstract

fetched live from OpenAlex

The primary objective of this study was to ascertain, through analysis of accessibility and development potential, which activity centres should be prioritised to support decentralisation of jobs, encourage better integration of transport and land use and ultimately aid the evaluation of a more compact, consolidation and connected city. In doing so, this study evaluated and compared the existing accessibility of different geographic units across the city, including the 34 activity centres identified by the Government of Australia, by the two most frequently use transport modes, namely – public transport and car. The analysis of this study has two parts. Firstly, an isochrone-based measure of accessibility was used for an accessibility modelling across the Perth Metropolitan Area in Western Australia. Secondly, using six node-place based indicators, this paper also endeavoured to prioritise the geographic units that are already better served by public transport, as indicated by the accessibility analysis. Multi-criteria weighed scoring method was applied to calculate a score out of 100 for each of the geographic units. The results of this analysis could help to identify activity centre(s) and other areas in Perth, if any, with higher potentials of being a Transit Oriented Development (TOD) supportive activity centre.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.222
GPT teacher head0.486
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2017
Admission routes1
Has abstractyes

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